Although the developed TSA approach is promising for transient analysis, its performance relies on the training data, like any other data-driven model. Here, as the transient data are generated from simulations, the training dataset is ensured to be balanced, i.e, the number of stable and unstable transients is close.
However, the real-world transient training datasets are imbalanced, i.e., the ratio between the unstable and stable events is less than 5%. This imbalance occurs since the unstable transient events are high-impact but low-frequency events. An imbalanced training dataset may adversely affect the classification accuracy. To address the problem of imbalanced data, two strategies, i.e., data resampling and redesigning the classifier algorithm, are suggested to improve the HGAN-based TSA method. The data resampling methods, such as oversampling and undersampling methods, can rebalance the ratio between the two types of events [1]}. In addition to resampling the training data, the classifier algorithm can be adjusted, such that the learning process is more sensitive to the correct identification of the unstable events. For instance, in the cost-sensitive method introduced in [2]}, the misclassification cost is modified such that a higher cost is assigned to the wrong prediction of the unstable events.
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